def test_rotation():
"""Test conversion between rotation angles and transformation matrix."""
tests = [(0, 0, 1), (.5, .5, .5), (np.pi, 0, -1.5)]
for rot in tests:
x, y, z = rot
m = rotation3d(x, y, z)
m4 = rotation(x, y, z)
assert_array_equal(m, m4[:3, :3])
back = rotation_angles(m)
assert_equal(back, rot)
back4 = rotation_angles(m4)
assert_equal(back4, rot)
def test_rotation():
"""Test conversion between rotation angles and transformation matrix."""
tests = [(0, 0, 1), (.5, .5, .5), (np.pi, 0, -1.5)]
for rot in tests:
x, y, z = rot
m = rotation3d(x, y, z)
m4 = rotation(x, y, z)
assert_array_equal(m, m4[:3, :3])
back = rotation_angles(m)
assert_equal(back, rot)
back4 = rotation_angles(m4)
assert_equal(back4, rot)
def initAvg_headpos(condition=[], folder=[]):
"""
Write the average head position based of the initial fit of several
independent files and save to a trans fif file
Parameters
----------
condition : str
String containing part of common filename, e.g. "task" for files
task_a.fif, task_b.fif, etc. Consistent naiming of files is mandatory!
If no condition is provided, it will average all files in folder.
folder : str
Path to input files. Default = current dir.
Returns
-------
None
"""
if not folder:
rawdir = getcwd()
else:
rawdir = folder
# Change to subject dir
if not condition:
files2combine = listdir(rawdir)
print('Using all files in %s' % rawdir)
else:
files2combine = glob.glob('%s*' % condition)
if not files2combine:
print('No files called \"%s\" found in %s' % (condition, rawdir))
return
elif len(files2combine) < 2:
warnings.warn(
'Only one file, please check!',
RuntimeWarning) # [!!!] should it just copy the initial headpos?
# Define output
mean_trans_folder = path.join(rawdir, 'trans_files')
if not path.exists(mean_trans_folder):
mkdir(mean_trans_folder)
mean_trans_file = path.join(mean_trans_folder, condition + '-trans.fif')
if op.isfile(mean_trans_file):
warnings.warn(
'N"%s\" already exists is %s. Delete if you want to rerun' %
(mean_trans_file, mean_trans_folder), RuntimeWarning)
return
files2combine = [f for f in files2combine
if '.fif' in f] # Make sure only fif files
files2combine.sort()
print('Files used for average head pos:')
for ib in range(len(files2combine)):
print('{:d}: {:s}'.format(ib + 1, files2combine[ib]))
init_xfm = []
init_rot = []
for ff in files2combine:
fname = path.join(rawdir, ff) # first file is enough NOT ANYMORE!
with warnings.catch_warnings(
): # suppress some annoying warnings for now
warnings.simplefilter("ignore")
info = Raw(fname,
preload=False,
verbose=False,
allow_maxshield=True).info
print(
'Ignore the warning above. We\'ll run MaxFilter in a few moments...'
)
init_xfm += [info['dev_head_t']['trans']]
# translations: info['dev_head_t']['trans'][:, 3][:-1]
init_rot += [info['dev_head_t']['trans'][:3, :3]]
mean_init_xfm = np.mean(np.stack(init_xfm),
axis=0) # stack, then average over new dim
init_rot_angles = [rotation_angles(m) for m in init_rot]
mean_init_rot_xfm = rotation3d(*tuple(
np.mean(np.stack(init_rot_angles),
axis=0))) # stack, then average, then make new xfm
assert (np.sum(mean_init_xfm[-1]) == 1.0) # sanity check result
mean_trans = info['dev_head_t'] # use the last info as a template
mean_trans['trans'] = mean_init_xfm # replace the transformation
mean_trans[
'trans'][:3, :3] = mean_init_rot_xfm # replace the rotation part
mean_init_headpos = mean_trans['trans'][:-1, -1] # meters
print('Mean head position (device coords): ({:.1f}, {:.1f}, {:.1f}) mm'.\
format(*tuple(mean_init_headpos*1e3)))
print('Discrepancies from mean:')
for ib, xfm in enumerate(init_xfm):
diff = 1e3 * (xfm[:-1, -1] - mean_init_headpos)
rmsdiff = np.linalg.norm(diff)
print('\tSession {:d}: norm {:.1f} mm ({:.1f}, {:.1f}, {:.1f}) mm '.\
format(ib + 1, rmsdiff, *tuple(diff)))
mean_rots = rotation_angles(
mean_trans['trans'][:3, :3]) # these are in radians
mean_rots_deg = tuple([180. * rot / np.pi
for rot in mean_rots]) # convert to deg
print('Mean head rotations (around x, y & z axes): ({:.1f}, {:.1f}, {:.1f}) deg'.\
format(*mean_rots_deg))
print('Block discrepancies from mean:')
for ib, rot in enumerate(init_rot):
cur_rots = rotation_angles(rot)
diff = tuple([
180. * cr / np.pi - mr for cr, mr in zip(cur_rots, mean_rots_deg)
])
print('\tSession {:d}: ({:.1f}, {:.1f}, {:.1f}) deg '.\
format(ib + 1, *tuple(diff)))
# Write trans file
write_trans(mean_trans_file, mean_trans)
print("Wrote " + mean_trans_file)
def contAvg_headpos(condition, method='median', folder=[], summary=False):
"""
Calculate average transformation from dewar to head coordinates, based
on the continous head position estimated from MaxFilter
Parameters
----------
condition : str
String containing part of common filename, e.g. "task" for files
task-1.fif, task-2.fif, etc. Consistent naiming of files is mandatory!
method : str
How to calculate "average, "mean" or "median" (default = "median")
folder : str
Path to input files. Default = current dir.
Returns
-------
MNE-Python transform object
4x4 transformation matrix
"""
# Check that the method works
method = method.lower()
if method not in ['median', 'mean']:
raise RuntimeError(
'Wrong method. Must be either \"mean\" or "median"!')
if not condition:
raise RuntimeError('You must provide a conditon!')
# Get and set folders
if not folder:
rawdir = getcwd() # [!] Match up with bash script !
else:
rawdir = folder
print(rawdir)
quatdir = op.join(rawdir, 'quat_files')
mean_trans_folder = op.join(rawdir, 'trans_files')
if not op.exists(mean_trans_folder): # Make sure output folder exists
mkdir(mean_trans_folder)
mean_trans_file = op.join(mean_trans_folder, condition + '-trans.fif')
if op.isfile(mean_trans_file):
warnings.warn(
'N"%s\" already exists is %s. Delete if you want to rerun' %
(mean_trans_file, mean_trans_folder), RuntimeWarning)
return
# Change to subject dir
files2combine = find_condition_files(quatdir, condition)
files2combine.sort()
if not files2combine:
raise RuntimeError('No files called \"%s\" found in %s' %
(condition, quatdir))
allfiles = []
for ff in files2combine:
fl = ff.split('_')[0]
tmplist = [f for f in listdir(quatdir) if fl in f and '_quat' in f]
#Fix order
if len(tmplist) > 1:
tmplist.sort()
if any("-" in f for f in tmplist):
firstfile = tmplist[
-1] # The file without a number will always be last!
tmpfs = sorted(tmplist[:-1],
key=lambda a: int(re.split('-|.fif', a)[-2])
) # Assuming consistent naming!!!
tmplist[0] = firstfile
tmplist[1:] = tmpfs
allfiles = allfiles + tmplist
if len(allfiles) > 1:
print('Files used for average head pos:')
for ib in range(len(allfiles)):
print('{:d}: {:s}'.format(ib + 1, allfiles[ib]))
else:
print('Will find average head pos in %s' % files2combine)
# LOAD DATA
# raw = read_raw_fif(op.join(quatdir,firstfile), preload=True, allow_maxshield=True, verbose=False).pick_types(meg=False, chpi=True)
# Use files2combine instead of allfiles as MNE will find split files automatically.
for idx, ffs in enumerate(files2combine):
if idx == 0:
raw = read_raw_fif(op.join(quatdir, ffs),
preload=True,
allow_maxshield=True).pick_types(meg=False,
chpi=True)
else:
raw.append(
read_raw_fif(op.join(quatdir, ffs),
preload=True,
allow_maxshield=True).pick_types(meg=False,
chpi=True))
quat, times = raw.get_data(return_times=True)
gof = quat[6, ] # Godness of fit channel
# fs = raw.info['sfreq']
# In case "record raw" started before "cHPI"
if np.any(gof < 0.98):
begsam = np.argmax(gof > 0.98)
raw.crop(tmin=raw.times[begsam])
quat = quat[:, begsam:].copy()
times = times[begsam:].copy()
# Make summaries
if summary:
plot_movement(quat, times, dirname=rawdir, identifier=condition)
total_dist_moved(quat,
times,
write=True,
dirname=rawdir,
identifier=condition)
# Get continous transformation
print('Reading transformation. This will take a while...')
H = np.empty([4, 4, len(times)]) # Initiate transforms
init_rot_angles = np.empty([len(times), 3])
for i, t in enumerate(times):
Hi = np.eye(4, 4)
Hi[0:3, 3] = quat[3:6, i].copy()
Hi[:3, :3] = quat_to_rot(quat[0:3, i])
init_rot_angles[i, :] = rotation_angles(Hi[:3, :3])
assert (np.sum(Hi[-1]) == 1.0) # sanity check result
H[:, :, i] = Hi.copy()
if method in ["mean"]:
H_mean = np.mean(H, axis=2) # stack, then average over new dim
mean_rot_xfm = rotation3d(*tuple(
np.mean(init_rot_angles,
axis=0))) # stack, then average, then make new xfm
elif method in ["median"]:
H_mean = np.median(H, axis=2) # stack, then average over new dim
mean_rot_xfm = rotation3d(*tuple(
np.median(init_rot_angles,
axis=0))) # stack, then average, then make new xfm
H_mean[:3, :3] = mean_rot_xfm
assert (np.sum(H_mean[-1]) == 1.0) # sanity check result
# Create the mean structure and save as .fif
mean_trans = raw.info['dev_head_t'] # use the last info as a template
mean_trans['trans'] = H_mean.copy()
# Write file
write_trans(mean_trans_file, mean_trans)
print("Wrote " + mean_trans_file)
return mean_trans
def contAvg_headpos(condition, method='median', folder=[]):
"""
Calculate average transformation from dewar to head coordinates, based
on the continous head position estimated from MaxFilter
Parameters
----------
condition : str
String containing part of common filename, e.g. "task" for files
task-1.fif, task-2.fif, etc. Consistent naiming of files is mandatory!
method : str
How to calculate "average, "mean" or "median" (default = "median")
folder : str
Path to input files. Default = current dir.
Returns
-------
MNE-Python transform object
4x4 transformation matrix
"""
# Check that the method works
if method not in ['median', 'mean']:
raise RuntimeError(
'Wrong method. Must be either \"mean\" or "median"!')
if not condition:
raise RuntimeError('You must provide a conditon!')
# Get and set folders
if not folder:
rawdir = getcwd() # [!] Match up with bash script !
else:
rawdir = folder
print(rawdir)
quatdir = op.join(rawdir, 'quat_files')
mean_trans_folder = op.join(rawdir, 'trans_files')
if not op.exists(mean_trans_folder): # Make sure output folder exists
mkdir(mean_trans_folder)
mean_trans_file = op.join(mean_trans_folder, condition + '-trans.fif')
if op.isfile(mean_trans_file):
raise RuntimeError(
'N"%s\" already exists is %s. Delete aif you want to rerun' %
(mean_trans_file, mean_trans_folder))
# Change to subject dir
# files2combine = glob.glob('%s*' % condition)
files2combine = [
f for f in listdir(quatdir) if condition in f and '_quat' in f
]
if not files2combine:
raise RuntimeError('No files called \"%s\" found in %s' %
(condition, quatdir))
elif len(files2combine) > 1:
print('Files used for average head pos:')
for ib in range(len(files2combine)):
print('{:d}: {:s}'.format(ib + 1, files2combine[ib]))
else:
print('Will find average head pos in %s' % files2combine)
# LOAD DATA
for idx, ffs in enumerate(files2combine):
# print op.join(quatdir,ffs)
if idx == 0:
raw = read_raw_fif(op.join(quatdir, ffs),
preload=True,
allow_maxshield=True).pick_types(meg=False,
chpi=True)
else:
raw.append(
read_raw_fif(op.join(quatdir, ffs),
preload=True,
allow_maxshield=True).pick_types(meg=False,
chpi=True))
quat, times = raw.get_data(return_times=True)
gof = quat[6, ] # Godness of fit channel
fs = raw.info['sfreq']
# In case "record raw" started before "cHPI"
if np.any(gof < 0.98):
begsam = np.argmax(gof > 0.98)
raw.crop(tmin=raw.times[begsam])
quat = quat[:, begsam:].copy()
times = times[begsam:].copy()
# Get continous transformation
print('Reading transformation. This will take a while...')
H = np.empty([4, 4, len(times)]) # Initiate transforms
init_rot_angles = np.empty([len(times), 3])
for i, t in enumerate(times):
Hi = np.eye(4, 4)
Hi[0:3, 3] = quat[3:6, i].copy()
Hi[:3, :3] = quat_to_rot(quat[0:3, i])
init_rot_angles[i, :] = rotation_angles(Hi[:3, :3])
assert (np.sum(Hi[-1]) == 1.0) # sanity check result
H[:, :, i] = Hi.copy()
if method in ["mean"]:
H_mean = np.mean(H, axis=2) # stack, then average over new dim
mean_rot_xfm = rotation3d(*tuple(
np.mean(init_rot_angles,
axis=0))) # stack, then average, then make new xfm
elif method in ["median"]:
H_mean = np.median(H, axis=2) # stack, then average over new dim
mean_rot_xfm = rotation3d(*tuple(
np.median(init_rot_angles,
axis=0))) # stack, then average, then make new xfm
H_mean[:3, :3] = mean_rot_xfm
assert (np.sum(H_mean[-1]) == 1.0) # sanity check result
# Create the mean structure and save as .fif
mean_trans = raw.info['dev_head_t'] # use the last info as a template
mean_trans['trans'] = H_mean.copy()
# plot_alignment(raw.info,subject='0406',subjects_dir='/home/mikkel/PD_motor/fs_subjects_dir/',dig=True, meg='helmet')
# Write file
write_trans(mean_trans_file, mean_trans)
print("Wrote " + mean_trans_file)
return mean_trans
